Research Topic

Thermoacoustic instability is a pressing problem in the gas turbines and the rocket industries. These instabilities arise due to the positive coupling of the heat source oscillations and the acoustic field of the confining chamber. This coupling gives rise to the high-pressure oscillations which limit the stable operating range of the engines. In turbulent combustors, the stable operating state is associated with chaotic variation in flow variables, whereas the high amplitude limit cycle oscillations (LCO) are characterized by the periodic ordered variation in flow variables. The transition from chaos to LCO shows the occurrence of intermittent bursts amidst the chaos. This change in the signal pattern from chaos to LCO has the potential to give early warnings regarding the impending instability. The rapidly advancing field of machine learning offers a systematic way for data-driven modelling, pattern recognition and anomaly detection.  These methods can be used to enhance the current understanding of the thermoacoustic systems and develop an early warning system.      

My research objective is to develop an understanding of the transition mechanism in thermoacoustic systems which can increase the stable operating range of the engines and their reliability.  

Research Advisor

Dr. R I Sujith

Department of Aerospace Engineering,

Indian Institute of Technology, Madras

Chennai - 600 036

Academic Background

M.E. Aerospace Engineering, Indian Institute of Science, Bangalore (2017) 

B.E.  Mechanical Engineering, Savitribai Phule Pune University , Pune (2015)

Research Interest

Combustion, Combustion instability,  Dynamical systems, Machine Learning

ORCID

0000-0003-3502-9009

Solution of Kuramoto–Sivashinsky equation

Institute Research Awards (Ph.D.) 2024-25 (July - November), IIT Madras

Visit to PIK, Potsdam, Germany